Environmental valuation methods, such as choice experiments, are increasingly being used to value complex and often unfamiliar environmental goods. A potential risk is that some survey respondents may not be capable of developing and expressing preferences for such goods. The noise from these individuals may then conceal the well-defined preferences of other respondents and affect valuation estimates. We address this problem by estimating a range of models that accounts for scale heterogeneity (which we interpret as a respondent's ability to choose: ATC) and taste heterogeneity. These models are applied to two case studies: amenity from coastal defence and biodiversity. In both case studies, model fit was improved in a scale-heterogeneity multinomial-logit (S-MNL) model (compared to a standard MNL model) suggesting the accounting for ATC (scale heterogeneity) improved preference revelation. A mixed multinomial-logit (MIXL) model outperformed the S-MNL model suggesting that accounting for taste heterogeneity was also important. However, a generalised multinomial-logit (G-MNL) model improved model fit over the MIXL model only for the biodiversity data suggesting that for these data both taste heterogeneity and ATC were important. We conclude that accounting for ATC can improve the reliability and robustness of the results when valuing complex or unfamiliar environmental goods.
|Number of pages||8|
|Early online date||20 Aug 2011|
|Publication status||Published - 15 Oct 2011|
- Choice experiments
- coastal defence